Forecasting House Prices in Italy

33 Pages Posted: 1 Aug 2019

Date Written: October 4, 2018

Abstract

Forecasting house prices is a difficult task given the strong relationship between real estate markets, economic activity and financial stability, but it is an important one. This paper evaluates the out-of-sample forecasting performance of various models of house prices in a quasi-real time setting. Focusing on Italy, we consider two structural models (using simultaneous equations) and a Bayesian VAR and compute both conditional and unconditional forecasts. We find that the models perform better than a simple autoregressive benchmark; however, the relative forecast accuracy depends on the forecast horizon and also changes over time. For the full sample period the simultaneous equation model, which takes into account credit supply restrictions and real estate taxation, shows the best performance measured in terms of root mean squared forecasting error (RMSFE). In the first part of the sample (2005-2010), medium-term forecasts of house prices greatly benefit from conditioning on the evolution of households’ disposable income, whereas from 2010 onwards the path of the stock of mortgages becomes important.

Keywords: house prices, forecasting, structural model, BVAR

JEL Classification: C32, C53, E37, R39

Suggested Citation

Emiliozzi, Simone and Guglielminetti, Elisa and Loberto, Michele, Forecasting House Prices in Italy (October 4, 2018). Bank of Italy Occasional Paper No. 463, Available at SSRN: https://ssrn.com/abstract=3429819 or http://dx.doi.org/10.2139/ssrn.3429819

Simone Emiliozzi

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Elisa Guglielminetti (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Michele Loberto

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
35
Abstract Views
394
PlumX Metrics